How Platform Drivers Survive under Algorithmic Management in China

• In Event: Labor 4.0: Algorithmic Management, Working Conditions, Experiences and Actions of Organization and Resistance

Abstract

Online-hailing taxis has been a prevailing phenomenon in China for years. Chinese platform drivers not only transport people but also feed data into the system to train algorithms and sustain platform’s operation and infrastructure (Chen & Qiu, 2019). Most existing studies focused on the platform control issues that drivers faced every day, such as blind passenger acceptance, price surging and rating system to imply platform drivers’ working condition. In contrast to a top-down view to conclude platform drivers’ difficulties as several general categories, I am more interested in specific problems emerged from dispatching system, working hours counting and rating system in daily practices, and how platform drivers tackle with these algorithmic issues to earn flexibility and make more money.

Ethnographic observation and qualitative interviews are two qualitative methodologies I apply to my research. In view of the data collection convenience, I chose a local platform as the main research subject in my home city, a second-tier provincial capital city in China with more than 4 million residences. Ethnographic observations are based on video recordings of naturally occurring interactions inside the cars for almost hundreds of hours. It will provide insights of the distance between drivers and pick-up passengers, navigation problems of system, car talks with related to their attitudes towards platforms, paying and rating issues. As a supplement, interviews with drivers will also help our understanding of their senses of platform control and algorithmic management that difficult to observe in video data.

Author

• YANI LIU